---
base_model: unsloth/Llama-3.2-11B-Vision-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- mllama
- vision-language
- document-understanding
- data-extraction
license: apache-2.0
language:
- en
library_name: transformers
model-index:
- name: PixelParse_AI
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: wis-k/instruction-following-eval
      split: train
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 43.83
      name: averaged accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FPixelParse_AI
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: SaylorTwift/bbh
      split: test
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 29.03
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FPixelParse_AI
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: lighteval/MATH-Hard
      split: test
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 14.43
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FPixelParse_AI
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      split: train
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 9.84
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FPixelParse_AI
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 9.25
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FPixelParse_AI
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 30.87
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FPixelParse_AI
      name: Open LLM Leaderboard
---


![image](./image.webp)
# Vision-Language Model for Document Data Extraction  

- **Developed by:** Daemontatox  
- **License:** apache-2.0  
- **Finetuned from model:** unsloth/Llama-3.2-11B-Vision-Instruct  

## Overview  

This Vision-Language Model (VLM) is purpose-built for extracting structured and unstructured data from various types of documents, including but not limited to:  
- Invoices  
- Timesheets  
- Contracts  
- Forms  
- Receipts  

By utilizing advanced multimodal learning capabilities, this model understands both text and visual layout features, enabling it to parse even complex document structures.  

## Key Features  

1. **Accurate Data Extraction:**  
   - Automatically detects and extracts key fields such as dates, names, amounts, itemized details, and more.  
   - Outputs data in clean and well-structured JSON format.  

2. **Robust Multimodal Understanding:**  
   - Processes both text and visual layout elements (tables, headers, footers).  
   - Adapts to various document formats and layouts without additional fine-tuning.  

3. **Optimized Performance:**  
   - Fine-tuned using [Unsloth](https://github.com/unslothai/unsloth), enabling 2x faster training.  
   - Employs Hugging Face’s TRL library for parameter-efficient fine-tuning.  

4. **Flexible Deployment:**  
   - Compatible with a wide range of platforms for integration into document processing pipelines.  
   - Optimized for inference on GPUs and high-performance environments.  

## Use Cases  

- **Enterprise Automation:** Automate data entry and document processing tasks in finance, HR, and legal domains.  
- **E-invoicing:** Extract critical invoice details for seamless integration with ERP systems.  
- **Compliance:** Extract and structure data for auditing and regulatory compliance reporting.  

## Training and Fine-Tuning  

The fine-tuning process leveraged Unsloth's efficiency optimizations, reducing training time while maintaining high accuracy. The model was trained on a diverse dataset of scanned documents and synthetic examples to ensure robustness across real-world scenarios.  



## Acknowledgments  

This model was fine-tuned using the powerful capabilities of the [Unsloth](https://github.com/unslothai/unsloth) framework, which significantly accelerates the training of large models.  

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)  

---  

# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/Daemontatox__PixelParse_AI-details)!
Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=Daemontatox/PixelParse_AI)!

|      Metric       |Value (%)|
|-------------------|--------:|
|**Average**        |    22.87|
|IFEval (0-Shot)    |    43.83|
|BBH (3-Shot)       |    29.03|
|MATH Lvl 5 (4-Shot)|    14.43|
|GPQA (0-shot)      |     9.84|
|MuSR (0-shot)      |     9.25|
|MMLU-PRO (5-shot)  |    30.87|